However, recently, inspired by the success of DL methods in other applications,
multimodal deep learning (MMDL) [1]} has attracted significant research attention due to its ability to learn common features from multiple modalities, with the potential to exploit their natural strengths and reduce redundancies. In this paper we aim to use MMDL methods for prediction of response to Cardiac Resynchronisation Therapy (CRT), which is a common treatment for HF. Our aim is to use MMDL to produce an automated tool for CRT response prediction that utilises only echocardiography data as input at inference time whilst also exploiting multimodal (CMR and echocardiography) data at training time.
